Search Results (208)

Food safety concerns related to pesticide residues in fruits and vegetables have increased globally, particularly in regions where monitoring programs are scarce or inconsistent. This study provides the first multi-year evaluation of pesticide contamination and associated dietary risks in Cape Verde, an African island nation increasingly reliant on imported produce. A total of 570 samples of fruits and vegetables—both locally produced and imported—were collected from major markets across the country between 2017 and 2020 and analyzed using validated multiresidue methods based on gas chromatography coupled to Ion Trap mass spectrometry (GC-IT-MS/MS), and both gas and liquid chromatography coupled to triple quadrupole tandem mass spectrometry (GC-QqQ-MS/MS and LC-QqQ-MS/MS). Residues were detected in 63.9% of fruits and 13.2% of vegetables, with imported fruits showing the highest contamination levels and diversity of compounds. Although only one sample exceeded the maximum residue limits (MRLs) set by the European Union, 80 different active substances were quantified—many of them not authorized under the current EU pesticide residue legislation. Dietary exposure was estimated using median residue levels and real consumption data from the national nutrition survey (ENCAVE 2019), enabling a refined risk assessment based on actual consumption patterns. The cumulative hazard index for the adult population was 0.416, below the toxicological threshold of concern. However, when adjusted for children aged 6–11 years—taking into account body weight and relative consumption—the cumulative index approached 1.0, suggesting a potential health risk for this vulnerable group. A limited number of compounds, including omethoate, oxamyl, imazalil, and dithiocarbamates, accounted for most of the risk. Many are banned or heavily restricted in the EU, highlighting regulatory asymmetries in global food trade. These findings underscore the urgent need for strengthened residue monitoring in Cape Verde, particularly for imported products, and support the adoption of risk-based food safety policies that consider population-specific vulnerabilities and mixture effects. The methodological framework used here can serve as a model for other low-resource countries seeking to integrate analytical data with dietary exposure in a One Health context. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

The characterization of airborne submicrometric composite structures’ debris is a challenge in the field of environmental monitoring and control. The work presented here aims to develop a new quantitative method to measure elemental mass concentrations via particle sampling and Transmission Electron Microscopy—Energy-Dispersive X-ray Spectroscopy (TEM-EDS). The principle is to collect airborne particles on a porous TEM grid, then add a certain mass of reference particles, and compare the relative mass percentages of elements from reference and sample particles via EDS. Diverse pairs of airborne particles (RbCl, CsCl, NaCl, SrCl₂, Ga(NO₃)₃, braking particles) were deposited on one TEM grid, and the experimental elemental mass ratios were measured by EDS and compared with the theoretical values. Results show that the quantitative and homogeneous collection of reference particles, such as RbCl, on the TEM grid could be suitable. For all the tested conditions, the absolute deviations between the theoretical elemental mass ratios and the experimental ratios remain lower than 8%. Thus, the mass concentration of Fe from the braking aerosol is calculated as 107 µg/m³. Compared to the cumbersome real-time instrument, this new method for mass characterization appears to be convenient, and requires a short time of aerosol sampling at the workplace. This approach ensures safety and practicability when assessing, e.g., the exposure risk of hazardous materials. Full article

In this study, we systematically investigated the characteristic parameter evolution laws of thermal runaway with respect to 18,650 lithium-ion batteries (LIBs) under thermal abuse conditions at five state-of-charge (SOC) levels: 0%, 25%, 50%, 75%, and 100%. In our experiments, we combined infrared thermography, mass loss analysis, temperature monitoring, and gas composition detection to reveal the mechanisms by which SOC affects the trigger time, critical temperature, maximum temperature, mass loss, and gas release characteristics of thermal runaway. The results showed that as the SOC increases, the critical and maximum temperatures of thermal runaway increase notably. At a 100% SOC, the highest temperature on the positive electrode side reached 1082.1 °C, and the mass loss increased from 6.90 g at 0% SOC to 25.75 g at 100% SOC, demonstrating a salient positive correlation. Gas analysis indicated that under high-SOC conditions (75% and 100%), the proportion of flammable gases such as CO and CH₄ produced during thermal runaway significantly increases, with the CO/CO₂ ratio exceeding 1, indicating intensified incomplete combustion and a significant increase in fire risk. In addition, flammability limit analysis revealed that the lower explosive limit for gases is lower (17–21%) at a low SOC (0%) and a high SOC (100%), indicating greater explosion risks. We also found that the composition of gases released during thermal runaway varies substantially at different SOC levels, with CO, CO₂, and CH₄ accounting for over 90% of the total gas volume, while toxic gases, such as HF, although present in smaller proportions, pose noteworthy hazards. Unlike prior studies that relied on post hoc analysis, this work integrates real-time multi-parameter monitoring (temperature, gas composition, and mass loss) and quantitative explosion risk modeling (flammability limits via the L-C formula). This approach reveals the unique dynamic SOC-dependent mechanisms of thermal runaway initiation and gas hazards. This study provides theoretical support for the source tracing of thermal runaway fires and the development of preventive LIB safety technology and emphasizes the critical influence of the charge state on the thermal safety of batteries. Full article

Ambient monitoring in chemical laboratories and industrial sites that use toxic, hazardous, or flammable materials is essential to protect the lives of workers, material resources, and infrastructure at these sites. In this research paper, we present an innovative approach for developing a low-cost and portable sensor node that detects and warns of hazardous chemical gas and vapor leaks. The system also enables leak location tracking using an indoor tracking and positioning system operating in ultra-wideband (UWB) technology. An array of sensors is used to detect gases, vapors, and airborne particles, while the leak location is identified through a UWB unit integrated with an Internet of Things (IoT) processor. This processor transmits real-time location data and sensor readings via wireless fidelity (Wi-Fi). The real-time indoor positioning system (IPS) can automatically select a tracking area based on the distances measured from the three nearest anchors of the movable sensor node. The environmental sensor data and distances between the node and the anchors are transmitted to the cloud in JSON format via the user datagram protocol (UDP), which allows the fastest possible data rate. A monitoring server was developed in Python to track the movement of the portable sensor node and display live measurements of the environment. The system was tested by selecting different paths between several adjacent areas with a chemical leakage of different volatile organic compounds (VOCs) in the test path. The experimental tests demonstrated good accuracy in both hazardous gas detection and location tracking. The system successfully issued a leak warning for all tested material samples with volumes up to 500 microliters and achieved a positional accuracy of approximately 50 cm under conditions without major obstacles obstructing the UWB signal between the active system units. Full article

Sudden coal and gas outbursts pose a significant hazard in deep-seated coal seam extraction, necessitating reliable risk assessment methods. Traditionally, assessments focus on gas-dynamic parameters, but experience shows they must be supplemented with tectonic factors such as fault-related disturbances, weak interlayers, and increased fracturing. Even minor faults in the Karaganda Basin can weaken the coal massif and trigger outbursts. The integration of 3D modeling enhances risk evaluation by incorporating both dynamic (gas-related) and static (tectonic) parameters. Based on exploratory drilling and geophysical studies, these models map coal seam geometry, fault positioning, and high-risk structural zones. In weakened coal areas, stress distribution changes can lead to avalanche-like gas releases, even under normal gas-dynamic conditions. An expert scoring system was used to convert geological and gas-dynamic data into a comprehensive risk index guiding preventive measures. An analysis of Karaganda Basin incidents (1959–2021) shows all outbursts occurred in geological disturbance zones, with 43% linked to fault proximity, 30% to minor tectonic shifts, and 21% to sudden coal seam changes. Advancing 3D modeling, geomechanical analysis, and microseismic monitoring will improve predictive accuracy, ensuring safer coal mining operations. Full article

In response to the pressing need for long-range, non-contact detection in hazardous gas leakage monitoring within chemical industrial parks, this study proposes a gas detection algorithm based on an infrared radiation physical model that utilizes dual-band infrared radiation background reconstruction. The proposed method addresses the issues of the existing detection methods’ lack of physical model support. First, appropriate filter wavelength ranges are selected based on the absorption spectral characteristics of the target gas. Subsequently, a physical model incorporating atmospheric attenuation, background radiation, and gas absorption properties is established based on gas radiative transfer theory. The non-absorption band data are then employed to reconstruct the theoretical background radiation of the absorption band. Furthermore, leveraging the synergistic observation advantages of a dual-band infrared imaging system, gas morphology identification is achieved by inverting the difference between the theoretical background and the actual measured values in the absorption band. Experimental results demonstrate that this method enables gas morphology detection through background reconstruction without requiring pre-collected gas-free background images. By implementing dual-band infrared radiation background reconstruction, this study achieves effective gas detection, providing a reliable technical approach for real-time monitoring and early warning of industrial gas leaks. The proposed algorithm enhances detection capabilities, offering significant potential for applications in industrial safety and environmental monitoring. Full article

The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution for real-time, proactive safety management. This paper evaluates AI models—Gemini 1.5 Pro, OPENAI GPT-4, and Copilot—in detecting workplace hazards, ensuring compliance with Process Safety Management (PSM) and DuPont safety frameworks. The study highlights the models’ potential in improving safety outcomes, reducing human error, and supporting continuous, data-driven risk management in petrochemical plants. This paper is the first of its kind to use the latest multimodal tech to identify the safety hazard; a similar model could be deployed in other manufacturing industries, especially the oil and gas (both upstream and downstream) industry, fertilizer industries, and production facilities. Full article

Gas sensors assume a crucial role in the medical domain, offering substantial support for disease diagnosis, treatment, medical environment management, and the operation of medical equipment by virtue of their distinctive gas detection capabilities. This paper presents an overview of the key research and development orientations for gas sensors, encompassing the exploration and optimization of novel sensitive materials, such as nanomaterials and metal oxides, to augment sensor sensitivity, selectivity, and stability. The innovation in sensor structural design, particularly the integration of micro-electromechanical systems (MEMS) technology to attain miniaturization and integration, is also addressed. The applications of gas sensors in health safety are expounded, covering the real-time monitoring of indoor air quality for harmful gases such as formaldehyde, as well as the detection of toxic gases in industrial environments to guarantee the safety of living and working spaces and prevent occupational health hazards. In the sphere of medical detection and diagnosis, this paper focuses on the detection of biomarkers in human exhaled breath by gas sensors, which facilitates the early diagnosis of diseases such as lung cancer. Additionally, the existing challenges and future development trends in this field are analyzed, with the aim of providing a comprehensive reference for the in-depth research and extensive application of gas sensors in the health, safety, and medical fields. Full article

Remote sensing imaging technology is one of the safest and most effective tools for gas leakage monitoring in chemical parks, as it enables fast and accurate access to detailed information about the gas cloud (e.g., volume, distribution, diffusion, and location) in the case of gas leakage. While multi-spectral imaging systems are commonly used for hazardous gas leakage detection, efforts to realize the three-dimensional reconstruction of gas clouds through data obtained from multi-spectral imaging systems remain scarce. In this study, we propose a method for realizing the three-dimensional reconstruction of gas clouds with only two multi-spectral imaging systems; in particular, the two multi-spectral imaging systems are used to simultaneously observe the three-dimensional space with gas leakage and reconstruct gas cloud images in real time. A geometric method is used for the localization in the monitoring space and the construction of a three-dimensional spatial grid. The non-axisymmetric inverse Abel transform (IAT) is then applied to the extracted gas absorbance images in order to realize the reconstruction of each layer, and these are then stacked to form a 3D gas cloud. Through the above measurement, identification, and reconstruction processes, a 3D gas cloud with geometric information and concentration distribution characteristics is generated. The results of simulation experiments and external field tests prove that gas clouds can be localized under the premise that they are completely covered by the field of view of both scanning systems, and the 3D distribution of the leakage gas cloud can be reconstructed quickly and accurately with the proposed system. Full article

Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this study focuses on a shallow soil landslide in Tongnan District, Chongqing Municipality. We employ a genetic algorithm (GA) to identify the most hazardous sliding surface through multi-iteration optimization. We discretize the landslide body into slice units using the dynamic slicing method (DSM) to estimate the runout distance. The model’s effectiveness is evaluated based on the relative errors between predicted and actual values, exploring the effects of soil moisture content and slice number on the kinematic model. The results show that under saturated soil conditions, the GA-identified hazardous sliding surface closely matches the actual surface, with a stability coefficient of 0.9888. As the number of slices increases, velocity fluctuations within the slices become more evident. With 100 slices, the predicted movement time of the Tongnan landslide is 12 s, and the runout distance is 5.91 m, with a relative error of about 7.45%, indicating the model’s reliability. The GA-DSM method proposed in this study improves the accuracy of landslide runout prediction. It supports the setting of appropriate safety distances and the implementation of preventive engineering measures, such as the construction of retaining walls or drainage systems, to minimize the damage caused by landslides. Moreover, the method provides a comprehensive technical framework for monitoring and early warning of similar geological hazards. It can be extended and optimized for all types of landslides under different terrain and geological conditions. It also promotes landslide prediction theory, which is of high application value and significance for practical use. Full article

With climate change, more intense weather events are observed, including pressure drops associated with the arrival of atmospheric fronts. These pressure drops are the primary cause of gas emissions from closed mines to the surface, with inactive mine shafts serving as the most likely emission pathways. The most significant emitted gases are carbon dioxide and methane, posing a dual challenge: greenhouse gas emissions and gas-related hazards. This study analyses changes in gas emission intensity in response to short-term (hourly) pressure fluctuations. Additionally, it presents the results of gas emission measurements from an inactive shaft, considering the impact of temperature differences between the air and emitted gases. The findings indicate that gas emissions are subject to inertia, which is crucial for gas monitoring around mine shafts, as emissions may still occur in the early stages of a pressure increase. Furthermore, the results show that temperature differences between the atmosphere and emitted gases could have a major influence on the process. Full article

In order to effectively monitor harmful gas leakage, this paper presents the design of an ultra-low-power IoT-based harmful gas monitoring system. The system is equipped with a custom-designed, low-power microcontroller motherboard, carefully selected low-power sensors, and high-efficiency, low-power communication modules. In addition, the system optimizes data acquisition and processing algorithms to segment gases of different concentrations. While ensuring real-time data acquisition and transmission, it achieves extremely low power consumption. By controlling the concentration of harmful gases and current for sensor performance testing, the experiment has shown that when the concentration of carbon monoxide reaches 500 ppm and methane reaches 2000 ppm, the system will trigger an alarm and upload relevant information; the sensor can detect and respond to the harmful gases within 60 s; and the system’s operating current fluctuation range remains within 0.5 mA, with an average power consumption much lower than that of other devices. Full article

To address the environmental hazards posed by oil spills and the limitations of conventional hydrocarbon monitoring techniques, a cost-effective and user-friendly gas sensor system was developed for the real-time detection and quantification of hydrocarbon contaminants in soil. This system utilizes carbon black (CB)-filled poly(methyl methacrylate) (PMMA) and poly(vinyl chloride) (PVC) nanocomposites to create chemoresistive sensors. The CB-PMMA and CB-PVC composites were synthesized and deposited as thin films onto interdigitated electrodes, with their morphologies characterized using scanning electron microscopy. The composites, optimized at a composition of 10% w/w CB and 90% w/w polymer, exhibited a sensitive response to hydrocarbon vapors across a tested range from C₂₀ (99 ppmV) to C₈ (8750 ppmV). The sensor’s response mechanism is primarily attributed to the swelling-induced resistance change of the amorphous polymer matrix in hydrocarbon vapors. These findings demonstrate the potential use of CB–polymer composites as field-deployable gas sensors, providing a rapid and efficient alternative to traditional gas chromatography methods for monitoring soil remediation efforts and mitigating the environmental impact of oil contamination. Full article

Monitoring hazardous gases is increasingly critical for environmental protection and human health. As a novel class of two-dimensional nanomaterials, layered metal chalcogenides have attracted substantial research attention in recent years. This is attributed to their unique physical and chemical characteristics, which endow them with remarkable potential for applications in gas sensing. In particular, bismuth sulfide (Bi₂S₃) has been extensively studied recently due to its cost-effectiveness, abundance, and eco-friendliness, aligning with the requirements of advanced sensing platforms. This article systematically summarizes recent advancements in gas sensors based on Bi₂S₃. Initially, the structural and functional properties of Bi₂S₃ are outlined, emphasizing its potential in detecting toxic gases. Subsequently, innovative methodologies aimed at enhancing room-temperature sensing efficiency are critically analyzed. The discussion concludes by addressing existing limitations and proposing future research directions to optimize Bi₂S₃ for practical applications. This review aims to systematically examine the design and optimization of next-generation gas detection nanomaterials, offering fundamental understanding of their performance enhancement mechanisms and exploring their potential implementation across multiple technological platforms. Full article